Claude Grasland & Etienne Toureille
19/10/2021
Previous analysis on german (left) and french (right) newspapers has demonstrated the interest to analyse networks of states and world regions :
But before to validate this results we need :
The objective of this short note is to explore the possibility of Wikidata for the production of multilingual dictionaries of world regions and more generally regional imaginations. Different types of “regions” related to the division of the Earth (“natural”) or the division of the World (“political”)
“Earth/Natural regions” : i.e. regions that are used in Atlas of Dictionaries for the localization at the surface on the earth and/or taught to young students in textbook as primary or secondary divisions of the surface of the Earth.
“World/Political regions” : i.e. groups of states that are linked by formal international organization (e.g. European Union, ASEAN, …) and/or are perceived as linked politically or economically by themselves of in the eye of external observers (e.g. BRICS, Eurasia).
But the difference is not clear : see. Grataloup (2011), Lewis and Wigen (2019), Copeaux (1997), Brennetot and Rosemberg (2013)
So-called “Physical maps” in Atlas are a good source :
Source : https://commons.wikimedia.org/wiki/Atlas_of_international_organizations
We propose to etablish a dictionary of Earth and World Regions in the five languages of interest for the project IMAGEUN :
We want to avoid any “eurocentric” or “anglocentric” perspective in the definition of entities. Therefore our definition of entities will follow the following rules :
To summarize, we propose to build partial equivalences between entities that belong to different lexical universes.
The comparison between lexical universes will be necessarily limited to a small sample of entities for which we can assume that the entities are approximately equivalent.
Wikidata defines itself as
The first interest of wikidata is to provide unique code of identifications of objects. For example a research about “Africa” will produce a list of different objects characterized by a unique code :
Once we have selected an entity (e.g. Q15) we obtain a new page with more detailed informations in english but also in all other languages available in Wikipedia.
A lot of information are available concerning the entity but, at this stage, the most important ones for our research are :
Wikidata data allows to formalize a procedure to build dictionaries and to objectify entity and translation choices between expert coders (us). It should lead to the construction of “specialized” dictionaries for the analysis of geographical entity, through discussion between the native speakers of the different languages in the project.
The specificity of the wikidata ontology is the fact that it is a multilinligual web where Q15 is a node of the web present in different linguistic layers. It means that we don’t have a single name or a single definition of Q15, except if we choose the english language as reference. Depending on the context (i.e. the language or sub-language), Q15 could be defined as :
| language | definition |
|---|---|
| fr | A continent named Afrique |
| en | A continent on the Earth’s northern and southern hemispheres named Africa or African continent |
| de | A “Kontinent auf der Nord- und Südhalbkugel der Erde” named “Afrika” |
| tr | A “Dünya nın kuzey ve güney yarıkürelerindeki bir kıta” named “Afrika” or “Afrika kıtası” |
| ar | The second largest continent in the world in terms of area and population, comes second only to Asia (trad.) |
The existence of the same code of wikipedia entities does not offer any guarantee of concordance between the geographical objects found in news published in different languages or different countries. But - and it is the important point - it help us to point similarities and differences between set of geographical entities that are more or less comparable in each language.
Ex. 1 Amazonie : In french language, Amazonie is associated to the entity Q2841453 which is defined as a “région naturelle en Amérique du Sud”. But this entity does not exist apparently in turkish language. At the same time the french language propose also an entity “Forêt amazonienne” Q177567 defined as forêt équatoriale située dans le bassin amazonien en Amérique du Sud which is present in tukish language. We have also a third entity bassin amazonien identified as Q244451 which refers to so-called régions naturelles based on water basin (Cholley 1939)
Ex.2 Proche-Orient/Moyen-Orient/Pays du Golfe/ Asie de l’Ouest : In french language we find four entities describing the complex geopolitical area located in western part of Asia and eastern part of Mediterranea. But this entities are not necessary used in all languages with the same frequency and can be completed by other entities like western Asia. The entity Proche-Orient (Q48214) which is frequent in french is only available in 30 languages when the Moyen-Orient (Q7204) is available in 84 languages, golfe Persique (Q34675) in 77 languages and Asie de l’Ouest (Q27293) in 71 languages.
Having in mind the limits of the equivalence of entities across languages, it can nevertheless be an interesting experience to select a set of wikipedia entities (Q15, Q258, Q4412 …) and to examine their relative frequency in our different media from different countries with different languages. A typical hypothesis could be something like :
which is not equivalent to the question
but rather equivalent to the two joint questions
We propose a semi-automatic method of extractions of entities in different languages that implies the presence of human expert at each step of the analysis. The figure below describe an example of research of world regions related to Africa in three languages.
The programs used for computer implementation are explained in the media cookbook on github with an example of implementation available onf the following page
We have realized a test of the previous workflow on an arbitraty selection of 110 entities :
Warning : This analysis does not offer perfect guarantee of quality because :
The purpose is therefore only to provide food for thought.
We start from a corpus of text where target wikipedia entities has been recognized :
| text | source | date | regs | nbregs |
|---|---|---|---|---|
| Europa und Südamerika: EU und Mercosur beschließen weltweit größte Freihandelszone | de_DEU_suddeu | 2019-06-28 | CO_EUR CO_AMR_south OR_EU OR_Merco | 4 |
| Asie, Afrique, Europe: la nouvelle stratégie de l’État islamique | fr_FRA_figaro | 2019-05-03 | CO_ASI CO_AFR CO_EUR | 3 |
| Présidentielle américaine: Europe, Asie, Otan. Le monde retient son souffle | fr_FRA_figaro | 2020-11-02 | CO_EUR CO_ASI OR_NATO | 3 |
| ‘Rolling emergency’ of locust swarms decimating Africa, Asia and Middle East | en_GBR_guardi | 2020-06-08 | CO_AFR CO_ASI LA_east_middle | 3 |
For the experience 2, we create a new object called hypercube where the text of news has been removed and where we keep only the number of tags or proportion of news speaking from one or several regions (where1, where2), by media (who) and by time period (when)
## Joining, by = "id"
We can propose firstly a table of top entities in the whole corpus of newspapers.
| code | type | label | nb | |
|---|---|---|---|---|
| 1 | OR_EU | org | European Union | 7914 |
| 2 | CO_EUR | cont | Europe | 5518 |
| 3 | CO_AFR | cont | Africa | 1822 |
| 4 | SE_medit | sea | Mediterranean Sea | 979 |
| 5 | OR_NATO | org | NATO | 697 |
| 6 | CO_ASI_minor | cont | Asia Minor | 499 |
| 7 | SE_black | sea | Black Sea | 480 |
| 8 | LA_east_middle | land | Middle East | 382 |
| 9 | CO_ASI | cont | Asia | 372 |
| 10 | CO_AMR | cont | Americas | 346 |
| 11 | LA_sahel | land | Sahel | 282 |
| 12 | LA_alpen | land | Alps | 253 |
| 13 | SE_arcti | sea | Arctic | 200 |
| 14 | LA_mashr | land | Maghreb | 160 |
| 15 | SE_pacif | sea | Pacific Ocean | 159 |
| 16 | CO_AMR_latin | cont | Latin America | 152 |
| 17 | LA_sahara | land | Sahara | 146 |
| 18 | SE_atlan | sea | Atlantic Ocean | 142 |
| 19 | CO_AFR_south | cont | Southern Africa | 130 |
| 20 | LA_amazon | land | Amazonia | 130 |
| tab1 | Cumhuryet_Region | Cumhuryet pct | Yeni Savak_Region | Yeni Savak pct |
|---|---|---|---|---|
| 1 | Europe | 30.4 | Europe | 25.1 |
| 2 | European Union | 20.8 | European Union | 20.8 |
| 3 | Asia Minor | 11.9 | Mediterranean Sea | 13.4 |
| 4 | Mediterranean Sea | 9.5 | Black Sea | 10.3 |
| 5 | NATO | 7.5 | NATO | 8.6 |
| 6 | Black Sea | 7.0 | Asia Minor | 7.1 |
| 7 | Africa | 2.4 | Africa | 4.4 |
| 8 | Asia | 1.8 | Asia | 1.6 |
| 9 | Eurasia | 1.3 | Eurasia | 1.3 |
| 10 | Southern Africa | 1.2 | Antarctica | 0.8 |
| tab1 | FAZ_Region | FAZ pct | Süd. Zeit._Region | Süd. Zeit. pct |
|---|---|---|---|---|
| 1 | European Union | 48.7 | European Union | 53.3 |
| 2 | Europe | 24.9 | Europe | 20.7 |
| 3 | Africa | 3.4 | Middle East | 4.3 |
| 4 | Americas | 3.3 | Africa | 3.6 |
| 5 | Southern Africa | 2.5 | Mediterranean Sea | 3.0 |
| 6 | Mediterranean Sea | 2.0 | Alps | 2.0 |
| 7 | Asia | 1.7 | Southern Africa | 1.4 |
| 8 | Middle East | 1.5 | Near East | 1.0 |
| 9 | Alps | 1.4 | South America | 1.0 |
| 10 | Eastern Europe | 0.8 | Asia | 0.8 |
| tab1 | Figaro_Region | Figaro pct | Le Monde_Region | Le Monde pct |
|---|---|---|---|---|
| 1 | Europe | 28.7 | Europe | 29.3 |
| 2 | European Union | 28.2 | European Union | 20.7 |
| 3 | Americas | 4.3 | Africa | 12.3 |
| 4 | Mediterranean Sea | 4.0 | Sahel | 4.1 |
| 5 | Africa | 3.5 | Mediterranean Sea | 3.4 |
| 6 | Alps | 3.1 | Alps | 3.1 |
| 7 | NATO | 2.7 | Americas | 2.6 |
| 8 | Amazonia | 2.7 | NATO | 2.0 |
| 9 | Sahel | 2.4 | Amazonia | 1.8 |
| 10 | Polynesia | 1.7 | Near East | 1.8 |
| tab1 | Guardian_Region | Guardian pct | Daily Telegraph_Region | Daily Telegraph pct |
|---|---|---|---|---|
| 1 | European Union | 39.2 | European Union | 49.3 |
| 2 | Europe | 22.8 | Europe | 25.9 |
| 3 | Africa | 5.7 | Africa | 7.2 |
| 4 | Arctic | 3.9 | Asia | 2.6 |
| 5 | Middle East | 3.8 | Middle East | 1.4 |
| 6 | Pacific Ocean | 3.4 | Arctic | 1.2 |
| 7 | NATO | 2.5 | Pacific Ocean | 1.1 |
| 8 | Americas | 2.2 | Commonwealth of Nations | 1.0 |
| 9 | Atlantic Ocean | 1.9 | NATO | 1.0 |
| 10 | Latin America | 1.8 | Caribbean | 1.0 |
| tab1 | Irish Times_Region | Irish Times pct | Belfast Telegraph_Region | Belfast Telegraph pct |
|---|---|---|---|---|
| 1 | European Union | 64.5 | European Union | 63.0 |
| 2 | Europe | 20.0 | Europe | 19.4 |
| 3 | Africa | 1.9 | Africa | 2.3 |
| 4 | NATO | 1.5 | Commonwealth of Nations | 1.9 |
| 5 | Asia | 1.3 | Arctic | 1.9 |
| 6 | Atlantic Ocean | 1.2 | NATO | 1.6 |
| 7 | Middle East | 1.2 | Middle East | 1.5 |
| 8 | Pacific Ocean | 0.9 | Asia | 1.4 |
| 9 | Maghreb | 0.6 | Caribbean | 1.1 |
| 10 | Americas | 0.6 | Atlantic Ocean | 0.9 |
| tab1 | Babnet (ar)_Region | Babnet (ar) pct | Econ. Mag_Region | Econ. Mag pct | La Presse_Region | La Presse pct | Réalités_Region | Réalités pct |
|---|---|---|---|---|---|---|---|---|
| 1.0 | Africa | 32.5 | Africa | 32.1 | Africa | 35.2 | Africa | 38.0 |
| 2.0 | European Union | 23.9 | European Union | 26.4 | Mediterranean Sea | 17.2 | European Union | 18.7 |
| 3.0 | Europe | 15.8 | Europe | 9.7 | European Union | 12.8 | Europe | 13.3 |
| 4.5 | Maghreb | 4.8 | Maghreb | 8.0 | Sahel | 9.0 | Sahel | 5.3 |
| 4.5 | Sahel | 4.8 | Mediterranean Sea | 7.2 | Europe | 6.6 | Maghreb | 5.1 |
| tab1 | Al Nahar (ar) | pct1 | El Kahbar (ar) | pct2 | El Watan (fr) | pct3 |
|---|---|---|---|---|---|---|
| 1 | Africa | 40.0 | Africa | 55.7 | Sahara | 25.6 |
| 2 | Europe | 27.5 | Europe | 19.5 | Africa | 19.0 |
| 3 | European Union | 11.7 | European Union | 7.6 | European Union | 11.7 |
| 4 | Sahel | 4.2 | Mediterranean Sea | 3.6 | Sahel | 9.5 |
| 5 | Asia | 3.9 | Asia | 3.2 | Europe | 8.9 |
| 6 | Middle East | 2.5 | Middle East | 2.6 | Maghreb | 7.9 |
| 6 | Arab League | 2.5 | Maghreb | 1.8 | Near East | 2.5 |
| 8 | Mediterranean Sea | 2.0 | Sahel | 1.8 | North Africa | 2.2 |
| 10 | Maghreb | 1.2 | Arab League | 1.2 | West Africa | 1.9 |
| 10 | NATO | 1.2 | NATO | 0.8 | Middle East | 1.9 |
N.B. We have eliminated the units “Americas,” “Europe” and “European Union”
## This version of bslib is designed to work with shiny version 1.5.0.9007 or higher.